class_docstr: BERT network training model.
  
  This is an implementation of the network structure surrounding a transformer
  encoder as described in "BERT: Pre-training of Deep Bidirectional Transformers
  for Language Understanding" (https://arxiv.org/abs/1810.04805).
  
  The BertTrainer allows a user to pass in a transformer stack, and instantiates
  the masked language model and classification networks that are used to create
  the training objectives.
  
  Attributes:
    network: A transformer network. This network should output a sequence output
      and a classification output. Furthermore, it should expose its embedding
      table via a "get_embedding_table" method.
    num_classes: Number of classes to predict from the classification network.
    num_token_predictions: Number of tokens to predict from the masked LM.
    activation: The activation (if any) to use in the masked LM and
      classification networks. If None, no activation will be used.
    initializer: The initializer (if any) to use in the masked LM and
      classification networks. Defaults to a Glorot uniform initializer.
    output: The output style for this network. Can be either 'logits' or
      'predictions'.
code: def get_config(self):
      return self._config
  
file_path: autodist/examples/benchmark/utils/modeling/networks/bert_pretrainer.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import tensorflow
  from utils.modeling import networks
label_desc: Use image node_id 2 for calling the BertPretrainer obj's underlying member method code with example usage: obj.get_config() and returns: self